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Identifying syntactic biomarkers of cognitive impairment in Mandarin-speaking older adults by applying machine learning approaches across multiple speech tasks
Tsy Yih, Hao Jan Yang, Lihe Huang, Quan Yao. Aphasiology, 2025-05-28
Topic: Risk, prevention and care in population studies
The use of linguistic biomarkers for detecting cognitive decline and early stages of dementia has demonstrated great potential due to its low-cost and non-invasive nature, especially towards a large aging population.Aims In this study, we focused on the syntactic complexity and aimed to identify highly discriminative syntactic features and those set in differentiating Mandarin-speaking older adults with and without cognitive impairment using machine learning models.Methods & procedures We established a new cohort consisting of 52 Chinese older participants, which has not been reported in any previous studies. Then we employed three machine learning algorithms to identify feature combinations performing the best in terms of high accuracy and AUC in cross-validation and minimal number of basic features.Outcomes & results No single feature or feature panels showed high discriminability between groups across all three tasks. Notably, a feature panel with only two features (mean depth of nodes, and proportions of prepositional dependencies) achieved the best result across classifiers in the story narrative task (ACC = 0.840, AUC = 0.867).Conclusions This study demonstrated the potential of using purely treebank-derived features within a single linguistic domain (syntax) for cost-effective cognitive impairment screening, indicating the value of syntactic biomarkers for cognitive impairment in Mandarin-Speaking Older Adults.
Key findings
- The use of linguistic biomarkers for detecting cognitive decline and early stages of dementia has demonstrated great potential due to its low-cost and non-invasive nature, especially towards a large aging population.Aims In this study, we focused on the syntactic complexity and aimed to identify highly discriminative syntactic features and those set in differentiating Mandarin-speaking older adults with and without cognitive impairment using machine learning models.Methods & procedures We established a new cohort consisting of 52 Chinese older participants, which has not been reported in any previous studies.
- Then we employed three machine learning algorithms to identify feature combinations performing the best in terms of high accuracy and AUC in cross-validation and minimal number of basic features.Outcomes & results No single feature or feature panels showed high discriminability between groups across all three tasks.
- Notably, a feature panel with only two features (mean depth of nodes, and proportions of prepositional dependencies) achieved the best result across classifiers in the story narrative task (ACC = 0.840, AUC = 0.867).Conclusions This study demonstrated the potential of using purely treebank-derived features within a single linguistic domain (syntax) for cost-effective cognitive impairment screening, indicating the value of syntactic biomarkers for cognitive impairment in Mandarin-Speaking Older Adults.
Why it matters
In a new cohort of 52 Mandarin-speaking older adults, a feature panel of two syntactic features achieved the best discrimination between those with and without cognitive impairment in a story narrative task, with accuracy of 0.840 and AUC of 0.867. This demonstrates the potential of syntactic biomarkers for cost-effective cognitive impairment screening, not for treating, curing, or preventing Alzheimer's disease.
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Study details
- Effect sizeAUC = 0.867
- Evidence strengthModerate
How this study scored 38/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, studied in people and randomised / blinded because the record does not state it. We have no data on senior author's track record, so that was left out: it earned 36 of the 96 points we could judge it on, which is 38 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Aphasiology) |
| Journal's citation record | 1 / 4 | Aphasiology: journal h-index 107 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 3 / 5 | Rated “moderate” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 4 / 5 | 2025 — within 3 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 3 / 3 | Effect reported with a statistic (interval, p-value, ratio or similar) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 2 / 7 | Cited 5 times (OpenAlex, 2026-10-10) |
| Citations per year | 1 / 5 | 1.7 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 3 / 6 | Cited 3.2× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 1 / 4 | Cited 5 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | 0 / 3 | Tongji University: institution h-index 628 (OpenAlex) |
| Multi-centre collaboration | 0 / 3 | OpenAlex lists 1 institution(s) in 1 country — scores zero (affiliation lists can be incomplete) |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 36 / 96 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 36 of the 96 points we could judge it on is 38 out of 100. |
| Total | 38 / 100 | — |
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Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
APA 7th
Yih, T., Yang, H. J., Huang, L., & Yao, Q. (2025). Identifying syntactic biomarkers of cognitive impairment in Mandarin-speaking older adults by applying machine learning approaches across multiple speech tasks. Aphasiology. https://doi.org/10.1080/02687038.2025.2511217
Vancouver
Yih T, Yang HJ, Huang L, Yao Q. Identifying syntactic biomarkers of cognitive impairment in Mandarin-speaking older adults by applying machine learning approaches across multiple speech tasks. Aphasiology. 2025. doi:10.1080/02687038.2025.2511217.
BibTeX
@article{yih2025-bf6fa90d,
author = {Yih, Tsy and Yang, Hao Jan and Huang, Lihe and Yao, Quan},
title = {{Identifying syntactic biomarkers of cognitive impairment in Mandarin-speaking older adults by applying machine learning approaches across multiple speech tasks}},
journal = {Aphasiology},
year = {2025},
doi = {10.1080/02687038.2025.2511217},
url = {https://doi.org/10.1080/02687038.2025.2511217}
}
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